Autonomous vehicles, also referred to as self-driving cars, navigate autonomously through an environment with minimal or no human input. To navigate autonomously, a vehicle determines a location within an environment so that various obstacles can be avoided and to ensure that the vehicle remains on the roadway. In general, autonomous vehicles use various sensors including, for example, LIDAR sensors, radar sensors, cameras, and other sensors to help the vehicle detect and identify obstacles and other features in the environment. Additionally, the vehicle may also use the sensors to precisely locate the vehicle within the environment.
In either case, each sensor has different characteristics that influence the accuracy and precision of tracks/trajectories produced from data of the separate sensors by associated trackers. For example, a LIDAR sensor may provide accurate positional data about an object but may provide speed estimation and long-range detection that are less reliable. Moreover, a RADAR sensor may provide accurate speed estimation while providing object detection that is less precise. Similarly, a camera sensor may provide accurate long range detection while experiencing less accuracy in other aspects.
Consequently, trajectories from the separate sensor data include tradeoffs between accuracy in position data, speed estimation, long-range detection, and so on. Moreover, combining information from the separate sensors/trackers together is a computationally intensive task that can encounter difficulties with determining how to combine the inputs and determining which input to trust.